Obstacles to Developing a Multinational Report Card on Antimicrobial Resistance for Canada: An Evidence-Based Review
Bibliographic record
Abstract
Many countries want to compare the results of their antimicrobial resistance programs to those of others nations to help gauge the effectiveness of their prevention and control practices. In our attempt to compare Canada with other nations, we encountered several challenges that must be addressed before meaningful multinational comparisons can be made. The fundamental barriers to comparison were the lack of shared targets for performance and predictive measures of success. Unique problems and policies within countries resulted in variations in goals, methods, pathogens, drugs, and priorities within and between jurisdictions. Other obstacles included: (1) lack of information on potential biases associated with different microbiological testing and sampling methods; (2) lack of information with which to conclude whether or not different programs examined comparable spectra of patients or outcomes; (3) inadequate description of the epidemiological rationale for sampling strategies; (4) use of aggregated national data that can hide regional or local variations; (5) rarity of studies designed explicitly for multinational comparison; and (6) lack of international agreement on methods, continuing education, and quality control needed to ensure program comparability. Comparison based on a country's ability to meet its internal goals for antimicrobial resistance control may be a more informative basis for a report card than specific resistance or drug use rates.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".